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Role: Technical Lead Agentic AI Platform (Python & AWS) CGEMJP00360889 Duration: 12 Months Location: 100% Remote Client Location: San Francisco, CA Job description: "We are seeking a hands-on Technical Lead to spearhead the architecture, development, and delivery of our enterprise Agent Deployment Framework and Active Data Products. In this role, you will lead a team of senior Python and cloud engineers focused on extending our existing Python-based DPS framework into a resilient, scalable, self-service platform. This platform will enable internal developer teams to build, test, and deploy AI agents, Model Context Protocol (MCP) integrations, and Slack applications effortlessly. The ideal candidate is a seasoned software architect and player-coach who thrives on distributed systems engineering, clean API design, and enterprise-grade automation. You will ensure that hundreds of independently owned agent packages can safely deploy to AWS AgentCore and Slack runtimes without requiring changes to shared platform code. Key Responsibilites: Architecture & Leadership: Technical Direction: Define the technical vision and architectural roadmap for the Agent Deployment Framework and Active Data Products. Team Management & Mentorship: Lead, unblock, and conduct rigorous code reviews for a high-performing team of Python, backend, and integration engineering contractors. Developer Experience (DX): Champion a seamless DX where developers author YAML configs, agent instructions, and tests, while the core platform abstracts away deployment, auth, and infrastructure complexities. Hands-on Core Engineering • CLI Tooling & Automation: Oversee and contribute to the development of robust CLI tools for package initialization, schema validation, local readiness checks, and live statlogging retrieval. • Enterprise CI/CD: Architect reliable GitHub Actions-based YAML deployment pipelines that flawlessly handle multi-environment updates, failure detection, and automated rollbacks. • Distributed Systems Security: Enforce secure credential management, ensuring agent owners manage their own AWS Secrets Manager secrets via approved logical platform bindings. • Integration Frameworks: Design reusable capabilities for Model Context Protocol (MCP), Slack APIs, asynchronous streaming, and complex per-user OAuth/OIDC flows. • Platform Multi-Tenancy: Ensure the core runtime securely isolates and supports hundreds of independent agent packages without monolithic overhead or global codebase alterations. • Observability: Implement comprehensive tracking, metrics, and logs across AWS ECS/Fargate, Lambda, and CloudWatch to ensure total operational visibility. • Deliver reliable GitHub Actions based deployments with testing, approvals, promotion, rollback, and SOX evidence. • Extend ingestion, dbt, Snowflake Dynamic Tables, data-quality, freshness, and self-healing capabilities. • Implement Governance as Code, including RBAC, ownership, SFSS-019 classification, PII, lineage, and catalog metadata. • Support Tableau semantic models through OSI/OSSIE and governed MCP/Slack activation."